Network Neuroscience and Personality

Sebastian Markett1, Christian Montag2,3 and Martin Reuter4

1Department of Psychology, Humboldt University, Berlin, Germany, 2Department of Molecular Psychology,Institute of Psychology and Education, Ulm University, Ulm, Germany, 3The Clinical Hospital of Chengdu BrainScience Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology ofChina, Chengdu, China and 4Department of Psychology, University of Bonn, Bonn, Germany

Abstract

Personality and individual differences originate from the brain. Despite major advances in theaffective and cognitive neurosciences, however, it is still not well understood how personalityand single personality traits are represented within the brain. Most research on brain-personality correlates has focused either on morphological aspects of the brain suchas increases or decreases in local gray matter volume, or has investigated how personalitytraits can account for individual differences in activation differences in various tasks.Here, we propose that personality neuroscience can be advanced by adding a networkperspective on brain structure and function, an endeavor that we label personality networkneuroscience.With the rise of resting-state functional magnetic resonance imaging (MRI), the

establishment of connectomics as a theoretical framework for structural and functionalconnectivity modeling, and recent advancements in the application of mathematical graphtheory to brain connectivity data, several new tools and techniques are readily available to beapplied in personality neuroscience. The present contribution introduces these concepts,reviews recent progress in their application to the study of individual differences, and explorestheir potential to advance our understanding of the neural implementation of personality.Trait theorists have long argued that personality traits are biophysical entities that are not

mere abstractions of and metaphors for human behavior. Traits are thought to actually existin the brain, presumably in the form of conceptual nervous systems. A conceptual nervoussystem refers to the attempt to describe parts of the central nervous system in functionalterms with relevance to psychology and behavior. We contend that personality networkneuroscience can characterize these conceptual nervous systems on a functional andanatomical level and has the potential do link dispositional neural correlates to actualbehavior.

1. Personality network neuroscience

Ever since the affective and cognitive neurosciences have embarked on their journey towardsunraveling the biological basis of cognition, motivation/emotion, and behavior, new techno-logies and paradigms have shaped their path. One of the hallmark developments on thetechnological side was MRI. It enables researchers to non-invasively assess neural processes inthe awake human brain (Turner & Jones, 2003). MRI is not perfect, just like any otherscientific method, but it is of indisputable value for the study of the human brain (Turner,2016). On the spatial level, MRI depicts anatomy of the living human brain with unprece-dented spatial detail. On the functional level, it can keep track of ongoing activity in braindynamics. Furthermore, MRI has been approved for use in healthy human research partici-pants who volunteer their time for scientific enquiry. In this regard it is superior to othertechniques like intracranial recordings, optogenetic imaging, or radioligand-based imagingwhich are either not approved for the use in healthy human volunteers, are not approved foruse in humans at all, or are severely constrained due to the emission of harmful radiation.Since its introduction in the early 1990s, MRI has positioned itself as the methodologicalbackbone of cognitive neuroscience and has provided marvelous insights into the neuralfoundation of psychological processes (Raichle, 2009).

On the paradigmatic side, the last 10 years have seen the rise of connectomics and networkneuroscience as a new way to reason about the brain. The neologism connectome, introducedindependently by Patric Hagmann and Olaf Spons in 2005 (Hagmann, 2005; Sporns, Tononi,& Kötter, 2005), combines the term “connection” with the suffix “-ome” which stands for “thewhole class of something.” In analogy to the term genome (“gene” and “-ome”) which describesthe entirety of and the organizational principle behind the genetic information of a species oran organism (Winkler, 1920), a connectome describes the organization of connectionsthroughout a nervous system. The brain is a complex network whose intricacy can beapprehended on many different resolution levels. On a microscopic level, neurons sprout

Personality Neuroscience

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Review Paper

Cite this article: Markett S, Montag C,Reuter M. (2018) Network Neuroscience andPersonality. Personality Neuroscience. Vol 1:e14, 1–14. doi:10.1017/pen.2018.12

Received: 30 January 2018Revised: 28 March 2018Accepted: 14 April 2018

Key words:brain connectivity; connectome; resting-statefMRI; personality traits; conceptual nervoussystem

Author for correspondence:Sebastian Markett, E-mail: sebastian.markett@hu-berlin.de

© The Author(s) 2018. This is an Open Accessarticle, distributed under the terms of theCreative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), whichpermits unrestricted re-use, distribution, andreproduction in any medium, provided theoriginal work is properly cited.

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axons that find their way to the dense dendritic trees of otherneurons where they connect via synaptic contacts. On a macro-scopic level, the axons of thousands of neurons merge together inmajor white matter fiber bundles that project from one brain areato another. MRI technology can be applied to non-invasively mapthese fiber bundles in the human brain and to estimate fromfunctional imaging data how information processing unfoldsalong these pathways. MRI connectomics has revealed that neuralconnections follow—despite all their complexity—certain orga-nizational principles that enable efficient and goal-orientedinformation processing. Recent years have seen major advancesin the field of network science. Sophisticated network modelingtools have been developed that are increasingly applied to brainconnectivity data in order to unravel organizational principlesand understand their relevance for psychological processes andclinical conditions.

Personality neuroscience as a new field of study within thecognitive and affective neurosciences has embraced neuroimaging,psycho-pharmacology, molecular genetics, and psychophysiologicalmethods as its tools of choice to study the biological foundations ofpersonality, and to derive explanatory models of individual differ-ences (DeYoung & Gray, 2009). In the present contribution, weargue that neural network modeling techniques should step up tojoin the methodological pantheon of our field. We will outline howconnectivity research can complement more traditional brainmapping approaches such as brain activation or simple morpholo-gical studies in assessing the biological bases of personality traits. Tothis end, we are going to introduce key concepts and findings ofresting-state functional MRI (fMRI), diffusion MRI and basic ideasof network neuroscience, with the aim of promoting the applicationof network neuroscience in general, and MRI-based connectomics inparticular, to the study of personality and as a basis of new types ofpersonality theory (see sections 4 and 5). We will build our argu-ments mostly on studies investigating non-ability traits, even thoughsimilar ideas should in principle be also applicable to other aspectsof individual differences.

2. Where nature meets nurture

Before we begin with introducing key concepts from brain con-nectivity, connectomics, and network science, we would like tobriefly review the goals and aims of personality neuroscience. Thekey presumption of personality neuroscience is that a personcannot be understood without understanding their brain(DeYoung, 2010). Personality neuroscience therefore utilizestechniques from affective and cognitive neuroscience to relatebrain processes to personality characteristics.

Personality psychology has developed sophisticated taxo-nomies to describe individual differences. Classificatory systemssuch as the Big Five model/five-factor model (Costa & McCrae,1992; Goldberg, 1993), the six-dimensional HEXACO model (Lee& Ashton, 2008), or multi-factor hierarchical models (Cattell,1965) mostly utilize self-report data to locate individuals in amulti-dimensional factor space. Although these models have beenvery successful in describing individual differences and also inpredicting actual behavior from individual personality scores,they are mostly non-explanatory and allow for only limitedinsights into why people differ. The personality neuroscienceapproach which seeks to trace the neural implementation ofpersonality factors in the brain is also not explicatory per se andoften falls short of providing insights into causal mechanisms.It does, however, give a more detailed picture on how personality

might work, and constitutes an attempt to unveil the neuralfoundation of personality that can be more easily traced to distaldeterminants of personality such as genetic and environmentalinfluences (DeYoung, 2010). Population genetic studies canprovide insights into such distal determinants of individualdifferences. A recent meta-analysis has compiled evidence from2,748 twin studies of the last 50 years and has quantified geneticinfluences on personality at 49% on average (Polderman et al.,2015). This estimate suggests that roughly one half of the variancein individual differences in personality can be explained bygenetic factors, while the other half should originate fromenvironmental influences. Such work is based on the decom-position of covariance matrices. Despite its tremendous success inpointing towards broad classes of influencing variables, it still fallsshort of pointing towards explanatory pathways and mechanisms.Moreover, findings like the above from twin research too easilysuggest for laypersons that nature and nurture are two distinctentities. But abundant research demonstrates that nature andnurture strongly interact (for a short overview, see Montag &Hahn, in press). Even when the recent advances in moleculargenetics (for review, see Reuter, Felten, & Montag, 2016) and theutilization of genome-wide association designs and geneticcomplex trait analysis (Plomin & Deary, 2015) would lead to anexhaustive list of genetic polymorphisms responsible for indivi-dual differences in the personality domain (which is an utopiaat the moment), we would still need to tackle the mechanismsby which these genetic variables interact with environmentalinfluences and cause individual differences in behavior andbehavioral dispositions. This problem also applies to environ-mental factors equivalently. Furthermore, new advances in thefield of epigenetics suggest an impact of the environment on geneactivity at a molecular level. Such gene-environment interactionsare currently studied by analyzing methylation patterns in pro-moter regions of genes and histone modification (Zhang &Meaney, 2010). Personality neuroscience, with its focus on neuralprocesses, will be of much value by describing personality andindividual differences at the brain level and hence providingintermediate mechanisms that bridge between personality andits more distal influences such as molecular genetics, gene byenvironment effects and the epigenome. This idea is also cur-rently utilized in the field of psychiatric genetics that facesquite similar challenges in linking genetic variation to complexbehavioral traits and individual differences (Meyer-Lindenberg &Weinberger, 2006).

In order to be successful in explaining individual differencesand personality, we need to ask how to best derive such inter-mediate neural models of personality. Here, we can get inspira-tion from behavioral biology. In the 1980s, behavioral biologistsand geneticists published a complete list of all neurons and theirsynaptic connections of the nematode Caenorhabditis elegans, awidely studied model organism in biology (Emmons, 2015;White, Southgate, Thomson, & Brenner, 1986). This roundwormpossesses only a small number of neurons (around 300) that formaround 5,000 chemical synapses. The rationale behind this effortwas straightforward: synaptic contacts between single neurons areestablished during learning and can thus depend on experience(Kandel & Schwartz, 1982). Genetic mechanisms are similarlyinvolved in synaptic plasticity and long-term potentiation(Alberini, 2009). Identifying a neural circuit with relevance forbehavior and then studying its genetic and experience-dependentdeterminers thus qualify as holistic approach with potential to goway beyond mere description (Bargmann & Marder, 2013).

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Studying nematode behavior and neural connectivity has atbest only limited potential for understanding human individualdifferences. The research strategy, on the other hand, could provevery fruitful, and clearly the study of C. elegans and Aplysiacalifornica led to groundbreaking insights (for an overview, seeHawkins, Kandel, & Bailey, 2006). At present, however, C. elegansis the only organism with a completely recovered network map atthe cellular level. Similar efforts in other model organisms such asDrosophila or the mouse are still pending, mainly because of thecomplexity of their nervous systems (Schröter, Paulsen, & Bull-more, 2017). Fortunately, such detail in mapping neural con-nectivity is not required to assess organizational principles ofbrain networks. White matter fiber tracts consist of thousands ofsingle axonal connections that share an area of origin and aprojection site. A large number of myelin sheets that insulate theaxons affect signals that can be picked up by MRI scanners.Macro-level brain connectivity in the form of white matter fibertracts can be revealed by modern MRI technology withoutdetailed knowledge of micro-level connectivity on the level ofsingle neurons (Sporns, 2013).

Previous work has successfully demonstrated thatexperienced-based changes in human behavior and ability areaccompanied by changes at the level of brain networks (Lewis,Baldassarre, Committeri, Romani, & Corbetta, 2009; Scholz,Klein, Behrens, & Johansen-Berg, 2009), and that individual dif-ferences in brain networks show associations with genetic varia-tion (Markett et al., 2016a, 2017a). Nature and nurture affectindividual differences and personality. Environmental influencesreach the brain through all afferent projections, mainly fromsensory sources. Each cell within the brain contains a completecopy of the genome and genetic information is constantly tran-scribed with marked effects on neural processing. It is within thehuman brain where nature and nurture meet. Models of the brainand its functions should thus provide an ideal background for thestudy of mental faculties such as human personality.

3. Personality traits and their neural implementation

Even though most definitions of personality emphasize a holisticperspective on individual differences, personality neurosciencehas so far mostly focused on the neural implementation ofpersonality traits (DeYoung, 2010). In our present line of argu-mentation, we also focus primarily on personality traits, whileacknowledging that this captures only parts of human personality.

The trait definition by Gordon Allport, one of our field’s earlyeminent trait theorists, views personality traits as neuropsycho-logical systems with the purpose of making a variety of stimulifunctionally equivalent, in order to trigger a consistent andmeaningful response (Allport, 1937). Functional equivalencemeans that a “common ground” of a class of stimuli is extracted,in order to initiate an appropriate response. As an example toillustrate this rather abstract definition, consider the emotionanxiety. There is a fair deal of stimuli that have the potential to beperceived as frightening. This can range from social situationssuch as public speech, to approach-avoidance conflicts such astaking an important exam, and to situations with high uncertaintyand potential danger such as a dark alley at night. These situa-tions might appear as entirely different things at first glance butcan very well trigger a feeling of anxiety. The trait conceptassumes that distinct circuitries in our brains (the neuropsycho-logical systems from the definition) are dedicated to extract the

frightening/worrying components from the stream of incominginformation and initiate a consistent response that might involvea reappraisal of the situation, careful exploration, and rumination,or—in case of an imminent threat—flight, fight, or freezing (Gray& McNaughton, 2000). Individual differences come into play bythe assumption that the reactivity or sensitivity of a givenneuropsychological system varies across people. People who aremore of the anxious type react with a stronger or more frequentanxiety response than less anxious people. The trait concept canthus account for at least three aspects of behavior: (a) that entirelydifferent situations can lead to a similar affective or motivationalreaction, (b) that people differ in the perception of externalstimuli as positive or threatening, and that (c) the strength andeven direction of the reaction to these stimuli is marked byindividual differences. The trait definition is of course not limitedto the description of dispositional differences in anxiety butapplies to other aspects of personality such as extraversion oropenness to experience as well.

This definition of a personality trait follows a neo-behavioristicline of thought. Neo-behaviorism positions that behavior can bebest described in terms of a S→O→R equation, where S denotes aStimulus, R an overt or covert reaction, and O an organism. Weuse the prefix neo- with the term behaviorism to emphasize thatthe trait definition does neither imply that the organism is a blackbox that is inaccessible to rigorous scientific examination, nor thatall behavior and behavioral disposition are solely the productof learning and conditioning processes. By emphasizing theorganism variable in the form of neuropsychological systems asthe main substrate of the trait, the brain is brought into the focusof trait research. Traits are not only statistical abstractions inthe imagination of psychometricians, but have a hard-wiredbiological analogue in the physical world that can be localized andassessed by neuroscientific methodology. This conceptualizationof traits as neuropsychological systems does not imply 1:1 cor-respondence between single brain structures and personalitytraits. Throughout the central nervous system, brain structuresare wired together into systems with certain functional roles. Onecentral assumption of personality neuroscience is the existence ofdistinct brain systems for distinct traits. This functional per-spective on brain systems is also reflected in the idea of theconceptual nervous system that seeks to explain the brain on theconceptual rather than the anatomical level (Gray, 1971; Gray &McNaughton, 2000).

Another corollary of the trait definition is the emancipation ofthe trait concept from trait-relevant stimuli and the organism’sbehavioral response. Even though traits have the purpose tooperate on environmental stimuli in order to trigger contingentresponses, the neuropsychological systems that constitute thetraits exist independently from the outside world (Mischel &Shoda, 1995). A key feature of personality traits is their temporalstability over longer stretches of an individual’s lifespan(Edmonds, Jackson, Fayard, & Roberts, 2008; Specht, Egloff, &Schmukle, 2011). An individual with a certain trait characteristicshould respond similarly over many instances of stimulus pre-sentation over the period of several months if not years. Exam-ining the interaction of the neuropsychological trait system andan environmental stimulus is therefore as informative for per-sonality neuroscience as the examination of neural systems in theabsence of stimulation. We emphasize this point because it isoften overlooked in traditional personality neuroscience research,and because we believe that tools from network neuroscience canmake a genuine contribution in this regard.

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The majority of functional imaging studies on personality sofar have mainly examined the interaction of trait expression andstimulus processing. The common paradigm entails the recordingof neural activity in response to a stimulus and the assessment ofthe extent to which the strength of the neural response dependson individual trait levels. The involved stimuli can range from thesimple passive viewing of emotional faces (e.g., Canli et al., 2001;Reuter et al., 2004) to complex naturalistic scenarios with actualbehavioral relevance for the participants (e.g., Mobbs et al., 2007),and various personality traits such as neuroticism (Haas, Omura,Constable, & Canli, 2007), extraversion (Cohen, Young, Baek,Kessler, & Ranganath, 2005), or self-transcendence (Montag,Reuter, & Axmacher, 2011) have been examined. This researchstrategy has led to a substantial body of research on the neuralcorrelates of individual differences. Taken together, the availableevidence unequivocally confirms that neural systems differ intheir reactivity to external stimulation depending on a person’spersonality (see Kennis, Rademaker, & Geuze, 2013 for review,and Calder, Ewbank, & Passamonti, 2011, for a coordinate-basedactivation likelihood estimation meta-analysis). This focus, how-ever, does not distinguish between stimulus processing and theneuropsychological trait system itself. Following the idea thattraits correspond to stable neuropsychological systems that existin the human brain independently from the outside world, neuralcounterpart of personality trait should be reflected in intrinsicproperties of the brain. For the longest time, morphologicalstudies of the brain have been the methodological approach ofchoice to assess such stable, stimulus-independent neural corre-lates of behavioral dispositions (e.g., DeYoung et al., 2010; Liuet al., 2013; Riccelli, Toschi, Nigro, Terracciano, & Passamonti,2017; for a meta-analysis, see Mincic, 2015). Voxel-based mor-phometry studies, in particular, provide detailed insights intoneuro-structural correlates of personality traits and have shownthat brain regions with trait-dependent activation profiles alsoshow trait-dependent differences in their morphology (Omura,Todd Constable, & Canli, 2005, but see also Liu et al., 2013 forreplication issues). Within biological systems it is often said thatfunction follows structure (Kristan & Katz, 2006), and the studyof structural personality correlates should thus be informative forthe understanding of functional alterations. The relationshipbetween macroscopic morphology and neural activity, however, isnot straightforward and is very likely to involve many inter-mediate steps. It will be necessary to consider additional aspectsof brain structure and function in order to fully map variousneuropsychological trait systems. Moreover, efforts to bringtogether structural and functional brain imaging data in person-ality neuroscience are scarce, until now.

Brain connectivity could represent an important step forward toa more comprehensive understanding of the neuroscientific basis ofhuman personality. Early biologically oriented personality psycho-logists have noted that it is always several brain areas (and not asingle one) that underlie fundamental personality traits. The rele-vance for a trait system might not become apparent from anatomyalone. Jeffrey Gray has coined the term “conceptual nervous system”to describe neural systems from the perspective of their functionaland behaviorally relevant purpose (Gray, 1971; Gray & McNaugh-ton, 2000). A conceptual nervous system implies several connectedbrain areas. From this perspective, Gray can be seen as an earlyproponent of a personality network neuroscience. The notion of“brain networks” is mostly implicitly assumed in neuroimagingstudies, but not explicitly assessed in terms of connectivity betweenmultiple brain areas. The connectome paradigm offers the

methodological toolbox to advance the field towards unraveling theconnectivity patterns underlying personality traits. Resting-statefMRI allows us to examine the intrinsic functional architectureof the human brain, independent from external stimulation. Weare going to introduce this and other techniques of relevance inconnectomics in the following.

4. Connectivity and networks: The connectome paradigm

Connectomes are network maps of brain connectivity. In general,connectomes can be studied on different scales, and the resolutionlevel determines what entity is considered a connection and whatentities are tied together by connections (Sporns, 2016). In studieson human brain networks, connectivity estimates are usuallyderived from neuroimaging data and the finest resolution level isthus restricted to single voxels, although a less-fine-grainedresolution at the level of larger brain areas is more common.

When looking at gross brain anatomy, it becomes apparentthat the location of gray matter is restricted to the cortical ribbonand subcortical structures (see Figure 1A). Neurons in layer threeof the cortical ribbon grow axons that leave cortical gray matterand descend to white matter areas before making contact withneurons in layer one or two of the cortical sheet in a distal part ofthe brain. Beginning with the work by early neuroanatomists,attempts were made to delineate the cortical ribbon into distinctbrain regions based on their cytoarchitecture (Brodmann, 1909;von Economo & Koskinas, 1925), or other properties such as gyri-and sulcification (Tziourio-Mazoyer et al., 2002), thickness of thecortical gray matter sheet (Fischl & Dale, 2000; Fischl et al., 2004,see Figure 1C), or the synthesis of multimodal brain imagingtechniques (Glasser et al., 2016). When assembling a connectomemap, one of such whole brain parcellation schemes is chosenand for each pair of brain regions it is determined whether theyare connected or not (Hagmann et al., 2010, see Figure 1D).Connectivity is either assessed on the structural or functionallevel. This distinction is crucial for the connectome paradigm.

The study of structural connectivity in the form of anatomicalwhite matter fiber projections has become feasible with the adventof diffusion-weighted MRI (Le Bihan et al., 1986), its refinementas diffusion tensor imaging (DTI; Basser, Mattiello, & LeBihan,1994) and the introduction of computer-assisted fiber-tracking(Basser, Pajevic, Pierpaoli, Duda, & Aldroubi, 2000). In DTI,water diffusion in biological tissue is described by a three-dimensional tensor model. DTI exploits the shape of the diffusiontensor which is affected by factors that restrict water diffusion in asample, such as the fat content of myelin sheets that insulateaxonal projections. Myelin sheets wrap around axons and thusrestrict water diffusion along white matter pathways. Each voxel’sprinciple diffusion direction can be obtained from its diffusiontensor and computational approaches can be used to reconstructmajor white matter projections in the brain (see Figure 1B).White matter tractography is by no means a new development inour field. The connectome paradigm, however, builds up on thesedevelopments and provides an integratory framework for theevaluation of white matter tracts throughout the entire brain.A shortcoming of the DTI method is its inability to detectdirectionality. From DTI alone, it cannot be inferred whether oneregions projects to the other or vice versa. At present, this canonly be achieved by means of invasive tract tracing and istherefore not applicable in human research participants.

Results from connectome fiber tracking can be depicted in amatrix that lists brain regions row- and columnwise, where

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matrix elements indicate whether two brain regions are connectedor not (see Figure 1F). Matrix elements can also give connectionweights, reflecting the absolute or relative strength of the con-nection (e.g., based on streamline count or on summary measuresof fiber tract integrity such as fractional anisotropy). Connectomematrices from diffusion MRI data are always symmetrical becauseit is not possible to derive information on the directionality ofstructural connectivity from this data source. Figure 2 illustrates afully assembled connectome as a circos plot.

Brain regions exchange information along the elements of theneuro-structural white matter scaffold (Park & Friston, 2013).The synchronization of neural activity from different brainregions is commonly interpreted as evidence for a functionalcoupling of these regions (Friston, Frith, Liddle, & Frackowiak,1993). The assessment of functional connectivity on the groundsof fMRI is thus correlational, and the most common approach isto compute simple linear correlations between blood oxygen leveldependent (BOLD) time series data from two or more brainregions (see Figure 3). For BOLD fMRI, whose temporal precisionis restricted by the sluggishness of brain hemodynamics, simplelinear correlations are sufficient. Imaging modalities with a highertemporal resolution than BOLD fMRI, however, can require moresophisticated coherence measures. The use of functional con-nectivity data in connectome studies has been tremendouslyadvanced by resting-state fMRI (Smith et al., 2013). Resting-statefMRI is an experimental neuroimaging protocol where BOLDactivity is recorded from the brains of research volunteers who do

not engage in a particular task (Fox & Raichle, 2007). In contrastto brain activation studies that aim at the isolation of neuralcorrelates of circumscribed cognitive and emotional processes,resting-state fMRI assesses intrinsic, non-stimulus-dependentneural activity. In a pioneering resting-state fMRI study, Biswal,Zerrin Yetkin, Haughton, and Hyde (1995) reported highlysynchronized brain activity in brain regions that are commonlyco-activated during simple motor tasks. Because this synchroni-zation occurred in the absence of any motor behavior or motorplanning, it was interpreted as an intrinsic somatomotor network(see Figure 3). Subsequent studies confirmed the robustness of thefinding and also provided evidence for other intrinsic connectivitynetworks such as the default mode network (Greicius, Krasnow,Reiss, & Menon, 2003), the insular-opercular network (Seeleyet al., 2007), the fronto-parietal network (Fox, Snyder, Vincent,Corbetta, & Van Essen, 2005), and networks in visual areas (forreview, see van den Heuvel & Hulshoff Pol, 2010). Depending onthe analysis method and resolution parameters, 7–12 robustintrinsic resting-state connectivity networks are commonly found.It has been shown that these networks correspond to task-relatedbrain activity, in a way that brain regions that connect together atrest activate together within and across experimental tasks(Gordon, Stollstorff, & Vaidya, 2012; Smith et al., 2009). Incomparison to task fMRI, resting-state activity places a muchhigher burden on the brain’s total metabolism budget (Raichle,2006), which indicates that the maintenance of intrinsic activity isbiologically costly which is usually a sign of functional

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Figure 1. Illustration of the workflow of connectome reconstruction. A connectome combines information from cortical gray and white matter (A). Diffusion imaging andcomputational reconstruction methods are applied to obtain a detailed map of white matter fiber connections (B). The cortical ribbon is parcellated into a set of non-overlapping regions of interest, (C) illustrates Freesurfer’s Desikan-Killiany atlas. Connectome reconstruction combines the information from (B) and (C) and determineswhether two ROIs are touched by any white matter fibers or not. (D) Shows two examples: the left and right superior frontal gyrus (left) are densely connected (E, left) while theleft medial orbitofrontal and superior parietal gyrus (D, right) are only sparsely connected (E). Results can be displayed in a connectivity matrix. Rows and columns are regionsof interest from the whole brain parcellation. Matrix elements indicate the presence (white) or absence (black) of a connection (F). The left matrix groups brain regionsaccording to hemisphere and cortical vs. subcortical, the right matrix according to their allegiance to network modules.

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Figure 2. Circos plot (Krzywinski et al., 2009) depiction of a structural connectome. In total, 82 brain regions of Freesurfer’s Desikan atlas are ordered according to hemisphereand lobe, and arranged on a circle. The red heat map illustrates degree centrality, a measure that quantifies the brain region’s number of connections (darker shades indicatehigher degree). The brain areas in dark gray are the brain regions with the highest degree (top 15%) that qualify as possible hub regions. Data are taken from Markett et al.(2017b).

Figure 3. Functional connectivity from blood oxygen level dependent (BOLD) functional magnetic resonance imaging is estimated by correlating the extracted BOLD time seriesfrom regions of interest or single voxels (left panel). The right panel shows prominent resting-state functional connectivity networks as revealed by independent componentanalysis (data courtesy of the authors). The networks in the top row correspond from left to right to the visual, the default mode, the somatomotor, and the dorsal attentionnetwork. The networks in the bottom row correspond to the left and right fronto-parietal, the frontal, and the salience network. All networks are displayed in radiologicalconvention (left is right).

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importance. In summary, the key finding of resting-state fMRI isthat single brain regions synchronize their activity even in theabsence of external stimulation. By applying multivariate statis-tics, it can be shown that this synchronization is organized intolarge-scale brain networks that together form the functionalconnectome. It has been shown that the functional connectomerelates closely to its structural counterpart (Honey et al., 2009;Horn, Ostwald, Reisert, & Blankenburg, 2014). Both types ofconnectivity, however, are non-redundant and provide uniqueinsights into the network level architecture of the human brain.Consider a train network as an analogy: in this analogy, structuralconnectivity between brain regions would represent railway tracksbetween various train stations. The train schedule, on the otherhand, would be akin to functional connectivity. The physicalrailway network places major constraint of the train schedule forsure, however, both types of information will be relevant forrailway engineers, traffic control, and passengers alike.

The connectome paradigm has gone further than just pro-viding a method toolbox for the assessment of structural andfunctional connectivity. More abstract organizational principles ofbrain networks can be described by the utilization of mathema-tical network theory. In network theory, a network consists of aset of network nodes such as brain regions that are fully orpartially connected by a set of links such as structural and/orfunctional connectivity (Albert & Barabasi, 2002). By applyingtransformations to the connectome matrix, a set of key observa-tions on brain networks have been observed: human brainnetworks have a relatively sparse connection density which meansthat most brain regions merely maintain direct connections to afew other brain regions (Hagmann et al., 2007). Only a smallamount of brain regions maintain many connections. Theseregions serve as network hubs in the brain and are crucial formaintaining a highly efficient information exchange across sub-networks (de Reus & van den Heuvel, 2014; Hagmann et al., 2008;van den Heuvel & Sporns, 2013, see Figure 2). The organizationof brain networks into local subnetworks and a set of integratoryhubs ensure both local and global efficiency in information flow,an organizational principle described as “small-world structure”(Sporns & Zwi, 2004). Small-world networks are characterized bya high connection density between neighboring nodes (char-acterized by high clustering coefficients, see Figure 4C), and shortcommunication paths between distant network nodes that enablequick information exchange across the entire network (char-acterized by short characteristic path length, see Figure 4A).

Several studies have sought to relate individual differences insuch anatomical and neuro-functional organization principles ofhuman brain networks to personality. We will review some ofthese findings in the next section.

5. Network level correlates of personality

The easiest and most basic connectomic studies make use of the seedmethod. This method defines a starting point in the brain (the seedregion) and then examines all structural or functional connectivityoriginating from this seed. Several studies have focused on therelationship between personality traits and amygdala connectivity,presumably based on the prominent role of the amygdala regionfor affective processing. Aghajani et al. (2013) report differentialpatterns of functional connectivity patterns originating from theamygdala depending on neuroticism and extraversion scores. Li,Qin, Jiang, Zhang, and Yu (2012) have provided a more fine-grained

perspective on amygdala connectivity by linking harm avoidance—ameasure related to neuroticism—to functional connectivity patternsof different amygdala subregions. Distinguishing between differentamygdala subregions is important as the amygdala consists of dif-ferent nuclei that play different roles in generating behavior (Ledoux,2003). Even though it is difficult to image the medial temporalregion due to frequent signal distortions (Olman, Davachi, & Inati,2009), it has been shown that different amygdala subregions havedistinct functional connectivity profiles at rest with unique pheno-typical associations (Eckstein et al., 2017; Roy et al., 2009). Amygdalafunctional connectivity was also shown to relate to trait anger(Fulwiler, King, & Zhang, 2012) and the SADNESS trait from theAffective Neuroscience Personality Scales (Deris, Montag, Reuter,Weber, & Markett, 2017; for the importance of Panksepp’s AffectiveNeuroscience Theory for personality neuroscience, see Montag &Panksepp, 2017a, 2017b). Another seed-based functional con-nectivity study from our group has used the anterior insula as seedto show insula-centered connectivity differences in harm avoidance(Markett et al., 2013). The most comprehensive resting-state func-tional connectivity study on personality to date has used multipleseed regions placed throughout central areas in major resting-statenetworks and reported wide-spread associations between all Big Fivepersonality traits (Adelstein et al., 2011). Based on their findings theauthors concluded that personality is reflected in the brain’s intrinsicfunctional architecture.

The link between neuroticism-related personality traits andamygdala as well as insula connectivity has been corroborated bystructural connectivity studies. Westlye, Bjørnebekk, Grydeland,

0 0.17 0.33 0.5 0.67 0.83 1

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A B

C

Figure 4. Toy networks for the illustration of measures from network theory. Theglobal efficiency of a network can be quantified by its characteristic path length (CPL).CPL is the average of all shortest path length in a network. The shortest path betweenany two nodes in a network equals the minimum number of edges that have to betransversed to reach one node from the other. In the network in (A), the shortest pathequals 1 between node 1 and node 2, equals 2 between node 1 and node 4, and 4between node 1 and node 7. The CPL of the network is 2.0476. The toy graph in (B)illustrates degree centrality and betweenness centrality. Degree centrality of a nodeequals the number of its connection to other nodes. The gray nodes have a degree ofone, the black nodes a degree of four, and the blue and red node a degree of 5. Ingeneral, it is assumed that high degree nodes are more important for the network as awhole. Betweenness centrality is a further centrality measure that can capturedifferent information on the importance of a node. Betweenness reflects the amountof shortest paths between any two nodes that travel through a given node. The rednode, for instance, has a higher betweenness than the blue nodes (even though theyhave the same degree). All shortest paths between any gray and any black node travelthrough the red and the blue node, however, all shortest paths between any twoblack nodes travel through the red but not through the blue node, hence its higherbetweenness. The networks in (C) illustrate the clustering coefficient, which isregarded a measure of local efficiency. The numbers indicate the clustering coefficientfor the red node in the network. The clustering coefficient gives the ratio ofneighboring nodes that share a connection themselves over the total number ofconnections that neighbors could share. In the first graph, none of the black nodes(the neighbors) share a connection, hence the clustering coefficient of 0. In the lastnetwork, all neighbors are directly connected to each other (six connections betweenneighbors), hence the clustering coefficient of one.

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Fjell, and Walhovd (2011) have linked harm avoidance to theintegrity of cortico-limbic white matter tracts such as uncinatefasciculus which connects mid-temporal structures including theamygdala to anterior regions. In a similar vein, Montag, Reuter,Weber, Markett, and Schoene-Bake (2012) report a link between acomposite measure of trait anxiety and the structural integrity ofthe uncinate fasciculus as well. And Baur, Hänggi, Langer, andJäncke (2013) report that structural connectivity between theamygdala and the anterior insula indexes trait anxiety. The ideathat mid-temporal to anterior connectivity is an essential brainconnectivity level correlate of trait anxiety and neuroticism hasalso been proposed by Montag, Reuter, Jurkiewicz, Markett, andPanksepp (2013) in a systematic review of the structural neuro-imaging literature on anxiety.

Personality network neuroscience promises an integrative,network level account of brain–personality relationships. Theconnectivity studies reviewed so far have exclusively focused onsingle brain regions and single brain connections in their study ofpersonality traits. However, they do point towards one large-scalebrain network called the insular salience network (Seeley et al.,2007). This network centers around the anterior insula, includesthe anterior cingulate, parts of the basal ganglia, and corticalregions along the operculum, and receives input from theamygdala via the uncinate fasciculus. In a recent report from ourgroup, we examined whether information processing efficiency inthe insular salience network as a whole relates to trait anxiety(Markett, Montag, Melchers, Weber, & Reuter, 2016b). Wemodeled the entire insula network as weighted graph and com-puted the network’s characteristic path length as a measure ofnetwork efficiency. Characteristic path length is a summarymeasure of all shortest-communication connections between allnetwork nodes in a given network (see Figure 4A). Using resting-state functional connectivity data, we found that harm avoidanceas an index of trait anxiety related negatively to the insulanetwork’s information exchange efficiency. Such system-levelapproaches have gained popularity in recent years. Beaty et al.(2016) have used a similar strategy to ours to link efficiency of thedefault mode network to openness to experience. Bey, Montag,Reuter, Weber, and Markett (2015) analyzed functional con-nectivity between large-scale functional brain networks andshowed that functional connectivity of the insular network as awhole relates to individual differences in the susceptibility tocognitive failure. Toschi, Riccelli, Indovina, Terracciano, andPassamonti (2018) used a similar approach to large-scale func-tional brain networks, but applied graph–theoretical assessmentof between-network connectivity to link organizational features ofthe functional connectome to the Big Five personality traits. Inthis work, conscientiousness was related to local aspects of thefronto-parietal and the default mode network. And Kyeong, Kim,Park, and Hwang (2014) examined functional connectivitybetween all cortical and subcortical brain regions in a whole brainparcellation scheme and showed that the intrinsic organization ofthe functional connectome into large-scale network is differentdepending on approach- and avoidance-related personality traits.Whole brain connectomic data were also analyzed by Gao et al.(2013). In a network analysis of functional connectivity between90 brain regions from the automatic anatomical labeling atlas,they found that extraversion relates positively to the global clus-tering coefficient, a measure of the clustering in a network (seeFigure 4C). Brain networks of more extraverted people showed ahigher local clustering of brain connectivity across the entirebrain, indicating that such a global organization property carries

information of personality. In addition to this global association,the authors also report relationships between both neuroticismand extraversion and the betweenness centrality of several brainregions (see Figure 4B). Betweenness centrality is a networkmeasure that quantifies how many shortest communication pathswithin a network run through a given brain node. In other words,the metric uses whole brain connectivity information to infer asingle region’s importance to network communication. A highlyinteresting aspect of the Gao et al. (2013) paper is the attempt topredict individual personality scores from topological aspects ofthe brain network. A separate prediction model was set up foreach participant by estimating a regression function from the dataof all other participants (leave-one-out-validation). Predictionaccuracies for extraversion was 11.4% and 21.7% for neuroticismacross all participants. These numbers are of course not very high,but given that only a subset of possible network aspects weresampled in a cross-sectional design, it shows that network aspectsof the brain do carry relevant information on personality. Afurther whole brain connectome study by Servaas et al. (2015)gives a detailed perspective of the neurotic brain. In their study,high neuroticism was associated with weaker functional connec-tions throughout the entire brain. In consequence, functionalsubnetworks were less clearly delineated and the whole brainnetwork had a higher resemblance to a network with randomconnection wiring. The aforementioned studies on whole-brainconnectivity illustrate that global properties of the brain’s con-nectivity architecture relate to personality traits, and emphasizethe importance of looking beyond connectivity of single brainregions such as the amygdala, or single functional brain networkssuch as the salience network. The discovery of large-scale con-nectivity networks (see Figure 3) might suggest that each of thesenetwork could have a circumscribed functional role, or mightpresent a trait system on its own. Mounting evidence suggest thatthis is not the case, as behavior and behavioral dispositions arerealized by the joint effort of several functional networks (Barrett& Satpute, 2013; Sylvester et al., 2012; Toschi et al., 2018; Tour-outoglou, Lindquist, Dickerson, & Barrett, 2015). Personalityneuroscience will need to understand the interplay between brainareas and between large-scale brain networks in order to derivecomplete network accounts of a neuropsychological trait systems.

At present, the literature on the relationship between per-sonality and network aspects of brain structure and functionplaces a strong emphasis on neuroticism and avoidance-relatedpersonality traits, and to a lesser extent on extraversion andapproach-related traits. This focus might reflect a research bias infavor of clinically relevant personality traits, but could also resultfrom more straightforward relationships between these person-ality traits and aspects of brain organization that are easilyassessable with the field’s current methodological toolbox(Bjørnebekk et al., 2012; Markett, Montag, & Reuter, 2016c).Neuroticism and trait anxiety seem to be linked to several aspectsof brain connectivity, ranging from associations between singlebrain areas and their connections, via the organization of singlebrain networks, to organizational principles of whole-brainconnectivity. We believe that the evidence available thus far isencouraging for further investigations into the connectomics ofpersonality. Further research will also want to reconcile themostly MRI-based connectomic findings with research fromother imaging modalities. There is a large body of resting-stateEEG studies that has repeatedly demonstrated a relationshipbetween frontal asymmetries in the alpha band and approach- andavoidance-related personality traits (Davidson, 2004; Wacker,

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Chavanon, Leue, & Stemmler, 2008). Frontal asymmetries arecommonly interpreted in terms of hemispheric dominance. Thiscould actually reflect the functional interplay of bilateral brainnetworks such as the fronto-parietal network or the insularsalience network. Most cortical regions show a high degree ofsynchronization with their contralateral counterpart in resting-stateBOLD time series (Zuo et al., 2010), and a first report suggests arelationship between novelty seeking, an approach-related person-ality trait, and the lateralization of functional connectivity of theanterior insula (Kann, Zhang, Manza, Leung, & Li, 2016).

As a last point, we want to come back to our initial proposalthat network maps of the brain might prove useful for the eva-luation of distal influences on personality such as genetic andenvironmental influences. An important first step would be toestablish not only correlates between brain networks andpersonality but also links between brain networks and geneticvariation and activity, and between brain networks andenvironmental influences. Studies have started to examine therelationship between the organization of brain connectivity andcortical gene expression (Forest et al., 2017; Romme, de Reus,Ophoff, Kahn, & van den Heuvel, 2017; Wang et al., 2015) orbetween brain connectivity and genetic variation (Markett et al.,2016a, 2017a, 2017b). Other studies have focused on experience-dependent changes in brain connectivity that might reflectHebbian plasticity (Dosenbach et al., 2007; Lewis et al., 2009;Taubert, Villringer, & Ragert, 2012). We believe that this firstevidence is encouraging for future investigations and justifiescautious enthusiasm that the connectome paradigm and a person-ality network neuroscience can make contributions to personalitypsychology that go beyond simple brain-personality associations.

6. Summary and outlook

In the present contribution, we have argued that network modeling ofbrain connectivity data has considerable potential for personalityneuroscience. We have iterated the theoretical foundations of person-ality neuroscience: that studying the human brain in the context ofpersonality research is more than another empirical layer of observa-tion and can be very informative for reconciling genetic and envir-onmental influences on personality. We have used the trait definitionto argue that more traditionally designed brain activation assays andsimple morphological studies are by themselves not sufficient to fullymap and understand the neural circuitry underlying personality traitsin the form of conceptual nervous systems. We have introduced theconnectome paradigm, described basic methodological approaches tostructural and functional connectivity, and their use in deriving net-work models of the human brain. After these theoretical considera-tions, we have provided a few examples of personality networkneuroscience studies that have been published in the last few years.

We hope that our readers found our argumentation compel-ling and our introduction into personality network neuroscienceappealing. We hope that our ideas might inspire a road map forthe quest towards unraveling the causal basics of human per-sonality. This being said, we have to acknowledge the manychallenges and open questions remain that will need to be solvedand answered in the future.

One question concerns the stability of the relationships withbrain networks and personality traits. As personality traits aredefined as relatively stable behavioral dispositions, any truerelationship between personality traits and network aspects of thebrain should show a similar degree of temporal stability. Thehuman brain is subject to plastic changes by experience and

learning (Kolb & Whishaw, 1998), which points to the question oftemporal stability in structural and functional networks. Studiessuggest stability of certain aspects of brain connectivity (Cao et al.,2014; Poppe et al., 2013) while other studies have reported acertain amount of plasticity in brain connectivity (Scholz et al.,2009). Lifespan studies on personality indicate that personalitychanges over the lifespan, particularly during maturation fromadolescence to early adulthood (Blonigen, Carlson, Hicks,Krueger, & Iacono, 2008; Roberts, Caspi, & Moffitt, 2001), andthat some changes in traits depends on the personality profile atyounger age (Donnellan, Conger, & Burzette, 2007; Lönnqvist,Mäkinen, Paunonen, Henriksson, & Verkasalo, 2008). Long-itudinal studies on personality and brain connectivity are stilloutstanding. Such longitudinal efforts should clarify whichaspects of brain networks show a similar stability with personalitytraits and change accordingly when personality changes over thelifespan (Specht et al., 2011).

A second question is the disentanglement of states and traits. Cole,Bassett, Power, Braver, and Petersen (2014) report that the brain-wide organization of functional connectivity into a set of functionalnetwork modules is highly similar across different cognitive andaffective task states, and resembles the modular structure of theresting state. On the other hand each task is characterized by subtleyet specific changes to the modular network structure. This workpoints at specific trait-like and state-like aspects of brain networkswhose contribution to the state-trait distinction in personality sciencehas to be assessed in future work. First studies on state-trait dis-tinctions in brain networks indicate that changes in the brain’sintrinsic architecture across several states depend on individual dif-ferences (Geerligs, Rubinov, Cam-CAN, & Henson, 2015), thatindividual differences in personality can predict the effect of state-inductions on intrinsic resting-state connectivity (Servaas et al., 2013),and that the distinction between structural and functional con-nectivity is also relevant to the disentanglement of states and traits(Baur et al., 2013).

A third question is the synthesis of brain connectivity data,behavioral dispositions, and actual behavior. The vast majority ofcognitive and personality network neuroscience studies still focuseson resting-state fMRI data and/or structural connectivity. Morerecently, however, studies have started to explore changes in thebrain’s intrinsic functional architecture during behavior (Bolt, Nomi,Rubinov, & Uddin, 2017; Cohen & D’Esposito, 2016; Cole et al.,2014; Geerligs et al., 2015). One study that also included a person-ality measure identified a network cluster of changed connectivitywithin the limbic system during the processing of emotional facialexpressions. Connectivity within this limbic network cluster showeda considerable test–retest reliability and correlated with trait anxiety(Cao et al., 2016). A complete personality network neuroscienceaccount should not only describe neuropsychological trait systems inthe resting brain but also demonstrate how these systems respond tostimulation and produce individual differences in behavior. Futureresearch can either focus on brain network properties that have beenlinked to self-report personality scores, and test whether these net-work properties change during trait-relevant stimulation andbehavior. Alternatively, brain regions with known functionalimplications for trait-relevant behavior can be studied from a net-work perspective. One example for such an endeavor would be thestudy of functional and structural connectivity between brain areasimplicated in the anxiety/fear-related activation gradient from theperiaqueductal gray to anterior areas of the cortex (Mobbs et al.,2009). Furthermore, we advocate to combine more real-life mea-sures that go beyond self-report with neuroscientific data such as

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resting-state fMRI. In recent years, powerful smartphone technolo-gies have started to allow researchers to record human behavior ineveryday life (Markowetz, Błaszkiewicz, Montag, Switala, &Schlaepfer, 2014; Yarkoni, 2012). With the upcoming of the Internetof Things, the combination of digital traces of a person with neu-roscientific data will become a natural research area at the interfacebetween psychology, neuroscience, and computer science (“Psy-choNeuroInformatics,” Montag et al., 2016). Of note, the feasibilityto combine brain imaging data with smartphone tracked variableshas been demonstrated recently (Montag et al., 2017).

Just like in other fields of psychology, a pressing issue of per-sonality neuroscience is sample size, statistical power, and replic-ability of individual differences findings. Many MRI studies sufferfrom a lack of statistical power (Cremers, Wager, & Yarkoni, 2017)and individual differences analyses of functional connectivity datarequire large sample sizes (Kelly, Biswal, Craddock, Castellanos, &Milham, 2012). Collaborations between personality researchersacross centers and open science data sharing efforts are needed tocomprehensively tackle the relationship between personality and thehuman brain. Important first steps in this direction are currentlyunderway (see Mendes et al., 2017, for the description of a largeopen science data set with a wealth of individual differences vari-ables). In the cognitive neurosciences, the last years have seen therise of large neuroinformatics platforms (Poldrack & Yarkoni,2016). A similar strategy for individual differences research wouldbe a desirable endeavor for future research.

Despite these open questions, network neuroscience approacheshave also promising prospects that go beyond fundamental researchon personality traits. An important application of connectomics ingeneral and resting-state fMRI in particular is neuroimaging inpopulations that show lower levels of compliance to followinstructions during complex task protocols (Fox & Greicius, 2010).Resting-state and DTI protocols usually entail the minimuminstruction to lie still for some minutes, which can be more easilyfollowed by patients with psychiatric or neurodegenerative symp-toms than complex behavioral task instructions. When sufficientprecautions are taken to minimize confounding head motion(Power, Barnes, Snyder, Schlaggar, & Petersen, 2012), connectivityMRI can reveal neural correlates of personality traits in impairedpatient populations. Personality traits can qualify as risk factors foror endophenotypes of psychiatric disorders (Benjamin, Ebstein, &Belmaker, 2001; Kendler, Neale, Kessler, Heath, & Eaves, 1993). Amore detailed account of the neural implementation of personalitytraits could thus make important contributions to psychiatricresearch and the clinical neurosciences.

In order to grow to its full potential, the connectome paradigmwill need to solve several methodological challenges. It has beenshown that methodological details such as thresholding connectivitymatrices and the resolution level of the whole brain parcellationscheme can bias results and interpretation (de Reus & van denHeuvel, 2013; van den Heuvel et al., 2017). Statistical issues includethe risk of alpha-error inflation due to the immense amount of datain network maps and the formulation of appropriate null models ofbrain connectivity to evaluate the statistical significance of topolo-gical features of brain networks (Fornito, Zalesky, & Breakspear,2013). Furthermore, biologically plausible and computationallyinexpensive methods for the estimation of effective connectivity areneeded, in order to assess the direction of functional connectivityestimates. And finally, data processing pipelines need to be opti-mized and unified to make the most of publicly available data setsand in order to ease replicability of findings across centers (Yan,Craddock, Zuo, Zang, & Milham, 2013).

A limitation of the present commentary is its narrow focus onnon-ability trait aspects of personality that relate to affective andmotivational behavior. Network aspects of individual differences incognition are just as relevant for personality network neuroscienceas the more narrow focus on personality taxonomies such as the BigFive. Several studies have addressed network aspects of ability traitssuch as working memory capacity, attentional ability, and generalintelligence (Hilger, Ekman, Fiebach, & Basten, 2017; Markett et al.,2014, 2018; van den Heuvel, Stam, Kahn, & Hulshoff Pol, 2009).This research can also be relevant for understanding more cognitiveaspects of the Big Five, such as conscientiousness, which is mostlyuncorrelated with cognitive ability (but see Chamorro‐Premuzic &Furnham, 2004), but paradoxically related to similar brain regions(Allen & DeYoung, 2017).

Despite of the many open questions and the early stage ofnetwork neuroscience, we believe that this paradigmatic extensionof our methodological toolbox will prove to be fruitful for our field.In a famous quote, personality psychologist Jeffrey Gray has posi-tioned that “in the long run, any account of behaviour which doesnot agree with the knowledge of the nervous and endocrine systemswhich has been gained through the direct study of physiology mustbe wrong” (1987, p. 241). Research from the past 10 years hasshown that the human brain is a network and that structural andphysiological network properties are relevant to its function.Accounts of human personality that are not in line with this evi-dence will at best remain incomplete.

Acknowledgments: The position of C.M. is funded by a Heisenberg grantawarded to him by the German Research Foundation (DFG, MO2363/3-2).

Conflicts of Interest: The authors have no conflicts of interest to declare.

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